Artificial Neural Networks Modelling for Nitrate Prediction in Surface Water of Gökırmak River (Türkiye)

Abstract

This study aimed to develop an artificial neural network (ANN) model to estimate the nitrate content in the surface water of the Gökırmak River. Samplings were carried out during 12 months from six stations between 2020 and 2021. Nitrate content varied between 0.20 and 2.70 mg l-1 while the mean value was 1.18 mg l-1 during the study period. The developed model consists of two input layers (month and station) and one output layer (nitrate content). Feed-forward backprop was used as the network type. Levenberg-Marquardt (TRAINLM) was used as a training function, LEARNGDM was used as an adaption learning function and mean squared error (MSE) was used as a performance function. The number of neurons was 10 and TANSIG was selected as transfer function. Epoch number adjusted 1000 iterations. ANN model predicted the nitrate content between 0.24 and 2.61 with a mean value of 1.16 mg l-1. The results showed that the best validation performance is 0.61264 at epoch 30. R values are 0.96257 and 0.84231 for training and testing, respectively. R-value was found 0.85352 for all data. In conclusion, this study presents the conception of an artificial neural network (ANN) model designed to predict nitrate concentrations in river water. The developed ANN model provides reasonable results for predicting the nitrate content using only given time and location inputs. More inputs can be included in future studies to ensure higher accuracy in the development of ANN models.